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1702402

全球精准畜牧业人工智慧（AI）市场－2025-2032

Global Artificial Intelligence (AI) In Precision Livestock Farming Market- 2025-2032

出版日期: 2025年04月10日 | 出版商:

DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2个工作天内

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简介目录

2024 年全球精准畜牧业人工智慧 (AI) 市场规模达到 22.3 亿美元，预计到 2032 年将达到 198.7 亿美元，在 2025-2032 年预测期内的复合年增长率为 15.39%。

受对高效和永续动物养殖实践日益增长的需求的推动，精准畜牧业中的全球人工智慧 (AI) 市场正在经历快速成长。人工智慧技术正在透过即时监控、预测分析和自动化彻底改变畜牧业管理，提高动物健康、生产力和资源优化。

饲养管理、疾病检测和行为监测等应用越来越受到关注，尤其是在大型农场。随着全球肉类和乳製品消费量的增加、劳动力短缺以及数据驱动农业的推动，预计市场将在北美、欧洲和亚太新兴经济体大幅扩张。

市场趋势

基于人工智慧的电脑视觉工具正在与摄影机集成，以追踪动物的行为和健康状况。爱尔兰公司 Cainthus 使用脸部辨识和视觉监控来评估乳牛行为、识别异常情况，并透过检测乳牛的不适或压力来提高产奶产量。

云端运算与人工智慧结合，实现了跨多个地点的综合牲畜管理。 Afimilk 的 AfiFarm 软体提供了一个集中式平台，可以分析来自感测器、挤奶机和餵食系统的资料，从而提供可行的见解和远端决策。

动力学

对高效能牲畜监控的需求不断增长

随着全球对牛奶、肉类和蛋类等动物产品的需求不断增加，农民面临着在保持动物健康的同时最大限度地提高生产力的压力。人工智慧工具有助于即时监控牲畜，从而实现早期疾病检测、优化餵食和及时繁殖週期。

例如，ZenaDrone 1000 透过即时 GPS 技术简化了牲畜追踪，即使在广阔或难以进入的区域也能确保精确的位置资料。配备GPS技术和大规模监控的无人机在这些地区具有很大的优势。 Zenadrone 的 GPS 追踪为牧场主提供了丢失动物的精确座标。

初始投资和维护成本高

高昂的初始投资和维护成本严重限制了人工智慧在精准畜牧业的应用，尤其是在中小型农户中。自动挤乳机、健康感测器和智慧监控平台等人工智慧整合系统价格昂贵，造成了财务障碍，尤其是在发展中地区。

此外，较长的投资回收期以及维护、软体订阅和资料管理的经常性成本阻碍了其广泛实施。融资管道有限和农村基础设施薄弱进一步增加了挑战，使得许多农民难以证明或承担此类先进技术的成本。

The global artificial intelligence (AI) in precision livestock farming market is experiencing rapid growth, driven by the increasing demand for efficient and sustainable animal farming practices. AI technologies are revolutionizing livestock management through real-time monitoring, predictive analytics, and automation, enhancing animal health, productivity, and resource optimization.

Applications like feeding management, disease detection, and behavior monitoring are gaining traction, especially in large-scale farms. With rising global meat and dairy consumption, labor shortages, and the push for data-driven farming, the market is projected to expand significantly across North America, Europe, and emerging economies in Asia-Pacific.

Market Trend

AI-based computer vision tools are being integrated with cameras to track animal behavior and well-being. Cainthus, an Irish company, uses facial recognition and visual monitoring to assess cow behavior, identify abnormalities, and improve milk yield by detecting discomfort or stress in dairy cattle.

Cloud computing combined with AI is enabling integrated livestock management across multiple locations. Afimilk's AfiFarm software offers a centralized platform that analyzes data from sensors, milking machines, and feeding systems, enabling actionable insights and remote decision-making.

Dynamics

Rising Demand for Efficient Livestock Monitoring

With the global increase in demand for animal products such as milk, meat, and eggs, there is pressure on farmers to maximize productivity while maintaining animal health. AI tools help monitor livestock in real-time, enabling early disease detection, optimized feeding, and timely reproduction cycles.

For instance, the ZenaDrone 1000 simplifies livestock tracking with real-time GPS technology, ensuring precise location data even in expansive or inaccessible areas. UAVs equipped with GPS technology and large-scale monitoring are highly advantageous in these areas. Zenadrone's GPS tracking provides ranchers with the exact coordinates of lost animals.

High Initial Investment and Maintenance Costs

High initial investment and maintenance costs significantly restrain the adoption of AI in precision livestock farming, particularly among small and medium-scale farmers. The expensive nature of AI-integrated systems-such as automated milking machines, health sensors, and smart monitoring platforms-creates a financial barrier, especially in developing regions.

Additionally, the long payback period and recurring expenses for maintenance, software subscriptions, and data management deter widespread implementation. Limited access to financing options and poor rural infrastructure further add to the challenge, making it difficult for many farmers to justify or sustain the cost of such advanced technologies.

Segment Analysis

The global artificial intelligence (AI) in precision livestock farming market is segmented based on component, deployment mode, livestock type, application and region.

Cloud-Based Solutions Accelerate Adoption of AI in Precision Livestock Farming

The cloud-based deployment mode is a key driver in the AI precision livestock farming market due to its scalability, real-time data access, and lower upfront infrastructure costs. Cloud platforms enable farmers to monitor and manage livestock remotely through smartphones or computers, making operations more efficient and responsive.

For instance, Afimilk's AfiCloud and Connecterra's Ida platform offer cloud-based solutions that collect and analyze data from sensors and wearable devices to deliver actionable insights on animal health, feeding, and reproduction. These systems allow continuous updates, seamless integration with multiple devices, and data storage without the need for expensive local servers.

Additionally, the cloud model supports multi-location farm management, which is increasingly essential for large commercial operations. As internet connectivity improves in rural areas and subscription-based pricing models become more accessible, cloud deployment continues to gain traction, driving digital transformation across the livestock farming industry.

Geographical Penetration

North America Leads AI Adoption in Precision Livestock Farming with Strong Tech Infrastructure and Agri-Tech Investments

North America dominates the AI in precision livestock farming market due to its advanced technological infrastructure, early adoption of smart farming solutions, and significant investments in agri-tech innovation. The US, in particular, is home to major players like Connecterra, Cargill, and Allflex, which are actively deploying AI tools for health monitoring, feeding optimization, and productivity tracking.

For instance, in 2024, Precision Livestock Technologies (PLT), a provider of software and hardware solutions for livestock feeding and health, has recently announced the launch of a new system that integrates artificial intelligence (AI) to forecast cattle feed intake and generate feeding recommendations. This system represents a significant advancement in the use of technology within the livestock industry. High digital literacy and greater access to funding make North America a frontrunner in this evolving market.

Sustainability Analysis

AI in precision livestock farming plays a crucial role in promoting sustainability by enabling resource-efficient, eco-friendly, and welfare-centric agricultural practices. Through real-time monitoring and predictive analytics, farmers can reduce overfeeding, optimize water usage, and minimize waste generation, thereby lowering the environmental footprint.

For example, AI-driven feeding systems ensure precise nutrient delivery, which reduces methane emissions and enhances feed conversion efficiency. Automated health monitoring helps detect diseases early, minimizing the need for antibiotics and veterinary interventions.

Competitive Landscape

The major global players in the market include Connecterra, Cainthus, Vence, DeLaval, Afimilk Ltd, BouMatic, Allflex Livestock Intelligence (MSD Animal Health), Quantified Ag, Cargill, Incorporated, GEA Group, Moocall and among others.

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Target Audience 2024

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Component
3.2. Snippet by Deployment Mode
3.3. Snippet by Livestock Type
3.4. Snippet by Application
3.5. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Rising Demand for Efficient Livestock Monitoring
- 4.1.2. Restraints
  - 4.1.2.1. High Initial Investment and Maintenance Costs
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory and Compliance Analysis
5.5. Sustainability Analysis
5.6. DMI Opinion

6. By Component

6.1. Introduction
- 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 6.1.2. Market Attractiveness Index, By Component
6.2. Hardware *
- 6.2.1. Introduction
- 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
- 6.2.3. Sensors
- 6.2.4. Smart Cameras
- 6.2.5. Drones
- 6.2.6. RFID Tags & Readers
- 6.2.7. GPS Devices
6.3. Software
- 6.3.1. AI Algorithms
- 6.3.2. Predictive Analytics
- 6.3.3. Farm Management Software
6.4. Services

7. By Deployment Mode

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 7.1.2. Market Attractiveness Index, By Deployment Mode
7.2. Cloud-Based *
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. On-Premise

8. By Livestock Type

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 8.1.2. Market Attractiveness Index, By Livestock Type
8.2. Cattle *
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Poultry
8.4. Swine
8.5. Sheep & Goats
8.6. Others

9. By Application

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.1.2. Market Attractiveness Index, By Application
9.2. Feeding Management *
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Milk Harvesting & Monitoring
9.4. Reproduction Management
9.5. Animal Health Monitoring & Disease Detection
9.6. Livestock Behavior & Welfare Monitoring
9.7. Supply Chain & Farm Management
9.8. Others

10. By Region

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 10.1.2. Market Attractiveness Index, By Region
10.2. North America
- 10.2.1. Introduction
- 10.2.2. Key Region-Specific Dynamics
- 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.2.7.1. US
  - 10.2.7.2. Canada
  - 10.2.7.3. Mexico
10.3. Europe
- 10.3.1. Introduction
- 10.3.2. Key Region-Specific Dynamics
- 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.3.7.1. Germany
  - 10.3.7.2. UK
  - 10.3.7.3. France
  - 10.3.7.4. Italy
  - 10.3.7.5. Spain
  - 10.3.7.6. Rest of Europe
10.4. South America
- 10.4.1. Introduction
- 10.4.2. Key Region-Specific Dynamics
- 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.4.7.1. Brazil
  - 10.4.7.2. Argentina
  - 10.4.7.3. Rest of South America
10.5. Asia-Pacific
- 10.5.1. Introduction
- 10.5.2. Key Region-Specific Dynamics
- 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.5.7.1. China
  - 10.5.7.2. India
  - 10.5.7.3. Japan
  - 10.5.7.4. Australia
  - 10.5.7.5. Rest of Asia-Pacific
10.6. Middle East and Africa
- 10.6.1. Introduction
- 10.6.2. Key Region-Specific Dynamics
- 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
- 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Livestock Type
- 10.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

11. Company Profiles

11.1. Connecterra *
- 11.1.1. Company Overview
- 11.1.2. Product Portfolio and Description
- 11.1.3. Financial Overview
- 11.1.4. Key Developments
11.2. Cainthus
11.3. Vence
11.4. DeLaval
11.5. Afimilk Ltd.
11.6. BouMatic
11.7. Allflex Livestock Intelligence (MSD Animal Health)
11.8. Quantified Ag
11.9. Cargill, Incorporated
11.10. GEA Group
11.11. Moocall